02103nas a2200289   4500000000100000000000100001008004100002260001500043653002600058653002000084653001100104653001500115653002100130653001300151100002000164700001500184700002100199700001900220700001800239700002300257700002000280245009000300300000700390490000700397520139500404022001401799       2023                            d  c2023-01-1010aDrug Delivery Systems10aDrug Liberation10aHumans10aInjections10aMachine Learning10aPolymers1 aPauric Bannigan1 aZeqing Bao1 aRiley J. Hickman1 aMatteo Aldeghi1 aFlorian Häse1 aAlán Aspuru-Guzik1 aChristine Allen00aMachine learning models to accelerate the design of polymeric long-acting injectables  a350 v143 aLong-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development.  a2041-1723